Business performance measurement practices in construction engineering organisations
Bibliographic record
Abstract
Purpose The need for performance improvement has led to the implementation of industry‐specific key performance indicators (KPIs) and greater awareness of the benefits of measurement in construction engineering organisations. This paper aims to present and discuss the findings of a survey based on the practical experiences of leading UK construction engineering organisations. Design/methodology/approach The paper is based on a questionnaire survey, the findings of which are discussed and analysed. The survey focused on establishing current industry practice and forms part of a larger study, which involved detailed case studies and led to the development of an innovative framework for links knowledge management initiatives with business performance measurement. Findings The survey shows that a significant proportion of organisations are now using a range of financial and non‐financial measures to assess business performance, and a growing number are adopting the excellence model and/or the balanced scorecard to facilitate a structured approach to implementing continuous improvement strategies. The paper identifies the barriers to the use of performance measurement models and discusses the differences between the practices in smaller and larger construction engineering firms. Originality/value The paper concludes with some practical considerations for implementing performance measurement models, which will be of value to business improvement managers and other senior managers in construction and other project‐based industries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".